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Active learning with SVM for land cover classification - what can go wrong?

 
: Wuttke, Sebastian; Middelmann, Wolfgang; Stilla, Uwe

:
Volltext urn:nbn:de:0011-n-4189256 (2.0 MByte PDF)
MD5 Fingerprint: 85a9e2b1f6b86fca5990341a64c3d2d9
Erstellt am: 4.11.2016


Krempl, G.:
AL@iKNOW 2016, Workshop on Active Learning: Applications, Foundations and Emerging Trends. Online resource : International Conference on Knowledge Technologies and Data-driven Business (i-KNOW), Graz, Austria, October 18, 2016, Proceedings
Graz, 2016 (CEUR Workshop Proceedings 1707)
http://ceur-ws.org/Vol-1707/
S.9-16
Workshop on Active Learning - Applications, Foundations and Emerging Trends (AL) <2016, Graz>
International Conference on Knowledge Technologies and Data-Driven Business (i-KNOW) <16, 2016, Graz>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Training machine learning algorithms for land cover classification is labour intensive. Applying active learning strategies tries to alleviate this, but can lead to unexpected results. We demonstrate what can go wrong when uncertainty sampling with an SVM is applied to real world remote sensing data. Possible causes and solutions are suggested.

: http://publica.fraunhofer.de/dokumente/N-418925.html